| Citation: |
LIAO Yanna, YAO Liang. Bridge disease detection and recognition based on improved YOLOX algorithm[J]. Journal of Applied Optics, 2023, 44(4): 792-800. DOI: 10.5768/JAO202344.0402004
|
In view of the low accuracy of the current bridge disease detection algorithm based on convolutional neural network, an improved YOLOX algorithm was proposed to improve the detection accuracy. By using the feature information of the shallow layer of the backbone network, the feature extraction enhancement network was improved, and the feature information of the same layer was added for fusion. An improved coordinate attention mechanism was introduced to combine the position information and the channel information to enhance the network recognition of bridge diseases. At the same time, the localization loss function was improved. The experimental results show that the accuracy of the improved YOLOX network structure for bridge disease detection reaches 92.11%, which is 4.40% higher than the original network.
| [1] |
卢文壮, 郑宗雨, 赵文茹, 等. 中国桥梁发展与标准化[J]. 标准科学,2021(增刊1):240-246.
LU Wenzhuang, ZHENG Zongyu, ZHAO Wenru, et al. Development and standardization of bridges in China[J]. Standard Science,2021(S1):240-246.
|
| [2] |
贾潇宇. 基于卷积神经网络的桥梁裂缝识别与测量方法研究[D]. 柳州: 广西科技大学, 2019.
JIA Xiaoyu. Research on bridge crack identification and measurement method based on convolutional neural network[D]. Liuzhou: Guangxi University of Science and Technology, 2019.
|
| [3] |
杨建华, 邹俊志. 基于机器学习的RC桥梁病害检测方法[J]. 北方交通,2020(6):18-20. doi: 10.15996/j.cnki.bfjt.2020.06.006
YANG Jianhua, ZOU Junzhi. RC bridge disease detection method based on machine learning[J]. North Traffic,2020(6):18-20. doi: 10.15996/j.cnki.bfjt.2020.06.006
|
| [4] |
杨紫艳, 马龙博, 邓凌, 等. 基于卷积神经网络的桥梁裂缝检测研究[J]. 山西建筑,2021,47(19):131-133. doi: 10.13719/j.cnki.1009-6825.2021.19.047
YANG Ziyan, MA Longbo, DENG Ling, et al. Research on bridge crack detection based on convolutional neural network[J]. Shanxi Architecture,2021,47(19):131-133. doi: 10.13719/j.cnki.1009-6825.2021.19.047
|
| [5] |
张宁. 基于Faster R-CNN的公路路面病害检测算法的研究[D]. 南昌: 华东交通大学, 2019.
ZHANG Ning. Research on highway pavement disease detection algorithm based on Faster R-CNN[D]. Nanchang: East China Jiaotong University, 2019.
|
| [6] |
ZHANG C, CHANG C, JAMSHIDI M. Concrete bridge surface damage detection using a single stage detector[J]. Computer Aided Civil and Infrastructure Engineering, 2020, 35(4):389-409.
|
| [7] |
罗晖, 贾晨, 李健. 基于改进YOLOv4的公路路面病害检测算法[J]. 激光与光电子学进展,2021,58(14):336-344.
LUO Hui, JIA Chen, LI Jian. Highway pavement disease detection algorithm based on improved YOLOv4[J]. Advances in Laser and Optoelectronics,2021,58(14):336-344.
|
| [8] |
周清松, 董绍江, 罗家元, 等. 改进YOLOv3的桥梁表观病害检测识别[J]. 重庆大学学报,2022,45(6):121-130.
ZHOU Qingsong, DONG Shaojiang, LUO Jiayuan, et al. Improved YOLOv3 for bridge apparent disease detection and identification[J]. Journal of Chongqing University,2022,45(6):121-130.
|
| [9] |
陈先昌. 基于卷积神经网络的深度学习算法与应用研究[D]. 杭州: 浙江工商大学, 2014.
CHEN Xianchang. Research on deep learning algorithm and application based on convolutional neural network[D]. Hangzhou: Zhejiang Gongshang University, 2014.
|
| [10] |
陆绮荣, 丁昕. 一种基于改进YOLOX的地下排水管道缺陷检测识别方法[J]. 电子测量技术, 2022, 45(21): 161-168.LU Qirong, DING Xin. An improved YOLOX-based method for defect detection and identification of underground drainage pipelines [J] . Electronic Measurement Technology, 2022, 45(21): 161-168.
|
| [11] |
WANG C Y , LIAO H , WU Y H , et al. CSPNet: A new backbone that can enhance learning capability of CNN[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). New York: IEEE, 2020.
|
| [12] |
蔡伟, 徐佩伟, 杨志勇, 等. 复杂背景下红外图像弱小目标检测[J]. 应用光学,2021,42(4):643-650. doi: 10.5768/JAO202142.0402002
CAI Wei, XU Peiwei, YANG Zhiyong, et al. Detection of weak and small targets in infrared images under complex background[J]. Journal of Applied Optics,2021,42(4):643-650. doi: 10.5768/JAO202142.0402002
|
| [13] |
TAN M, PANG R, LE Q V. Efficientdet: Scalable and efficient object detection[C]//Procee dings of the IEEE/CVF conference on computer vision and pattern recognition.New York: IEEE, 2020: 10781-10790.
|
| [14] |
HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE co nference on computer vision and pattern recognition. New York: IEEE, 2018: 7132-7141.
|
| [15] |
WOO S, PARK J, LEE J Y, et al. Cbam: Convolutional block attention module[C]//Procee dings of the European conference on computer vision (ECCV). New York: IEEE,2018: 3-19.
|
| [16] |
HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2021: 13713-13722.
|
| [17] |
MUNDT M, MAJUMDER S, MURALI S, et al. Meta-learning convolutional neural archit ectures for multi-target concrete defect classification with the concrete defect bridge image dataset[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re cognition. New York: IEEE, 2019: 11196-11205.
|
| [1] | LI Chao, ZHANG Yuguo, LIU Baowei, CAO Jing, GUO Yapin, WANG Jiapeng. Research on technology of large area blackbody radiation source used in field calibration for target radiation characteristics[J]. Journal of Applied Optics, 2021, 42(2): 292-298. DOI: 10.5768/JAO202142.0203001 |
| [2] | CAO Pan, XU Shijun, SHI Xiaohong, YUAN Liang, ZHAN Chunlian. Research on spatial uniformity test of grating imaging spectrometer[J]. Journal of Applied Optics, 2020, 41(2): 354-360. DOI: 10.5768/JAO202041.0203003 |
| [3] | GAN Tao, YUAN Yinlin, ZHAI Wenchao, ZHENG Xiaobing, MENG Fangang, WU Haoyu. Design and test of in-site radiometric calibration reference light source for spaceborne low light level remote sensors[J]. Journal of Applied Optics, 2020, 41(1): 140-144. DOI: 10.5768/JAO202041.0103002 |
| [4] | Chen Ligang, Feng Weiwei. Experimental study on multi-optical parameter imaging technology under fog and haze weather[J]. Journal of Applied Optics, 2017, 38(4): 613-616. DOI: 10.5768/JAO201738.0403002 |
| [5] | J. A. A. Engelbrecht, G. Deyzel, E. G. Minnaar, W. E. Goosen, I. J. Van Rooyen. Assessment of neutron-irradiated 3C-SiC implanted at 800 ℃[J]. Journal of Applied Optics, 2015, 36(6): 937-941. DOI: 10.5768/JAO201536.0604001 |
| [6] | FAN Ji-hong, ZHAO Sheng-lu, ZHAN Chun-lian, YUAN Liang, LI Zheng-qi, LU Fei, LI Yan. Absolute radiometric calibration technique of imaging spectrometer[J]. Journal of Applied Optics, 2013, 34(4): 629-632. |
| [7] | ZHANG Fang, GAO Jiao-bo, WANG Jun, XIAO Xiang-guo, ZHANG Lei. AAbsolute spectral radiation calibration of fiber spectrometer[J]. Journal of Applied Optics, 2011, 32(1): 101-105. |
| [8] | WEI Mao-jin, YANG Wei-wei, LIU De-gong. The research measuring refractive indexof medium based on reflectivity of linear polarized light[J]. Journal of Applied Optics, 2010, 31(1): 100-104. |
| [9] | ZHANG Xiao-ying, ZHU Ding-qiang, CAI Guo-biao. Calculation for visible radiation of midcourse target[J]. Journal of Applied Optics, 2008, 29(3): 444-447. |
| [10] | HONG Wen-xue, CAI Jian-hong, JING Jun. A Research on Heat Radiation Spectrum Characteristics of Moxibustion Therapy[J]. Journal of Applied Optics, 2004, 25(4): 1-3. |
WeChat
You are the 2900304th visitor, Welcome!
Address: No.9, west section of No.3 electronic road, Xi'an Post Code: 710065 Tel: 029-88288172
Copyright © Editorial Department of Journal of Applied Optics
陕公网安备 61011302001501号 
DownLoad: